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mayankptdr/README.md

MAYANK PATIDAR

About Me:

I am an AI Engineer passionate about building intelligent systems that solve real-world problems. My work spans healthcare AI, computer vision, machine learning, and generative AI, with a focus on transforming research ideas into production-ready products.
I have developed AI-powered healthcare platforms, medical imaging solutions, multimodal applications, and end-to-end machine learning systems using technologies such as Python, PyTorch, FastAPI, TensorFlow, and modern AI frameworks.
I enjoy working at the intersection of AI, software engineering, and product development, where I can take an idea from concept to deployment and create solutions that deliver measurable impact.

💻 Tech Stack:

Python Dart CSS3 HTML5 Firebase Netlify Render Vercel Anaconda nVIDIA FastAPI Flask Flutter OpenCV React Streamlit Firebase MySQL MongoDB Postgres Supabase Keras Matplotlib mlflow NumPy Pandas PyTorch scikit-learn Scipy TensorFlow Git GitHub

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  1. RSNA RSNA Public

    Multi-label Knee MRI Abnormality Detection using DINOv2, Gemini-powered weak labels, and large-scale DICOM processing. Public LB AUROC: 0.823.

    Jupyter Notebook

  2. SnapClass_face_n_voice_attendance SnapClass_face_n_voice_attendance Public

    Production-grade attendance platform integrating Dlib face recognition, Resemblyzer speaker verification, PostgreSQL, Supabase, and Streamlit.

    Python

  3. SATARKA SATARKA Public

    SATARKA - Your Personal Health Operating System for report analysis, health tracking, AI assistance and emergency healthcare support.

    Dart

  4. Satarka-backend Satarka-backend Public

    Satarka - Your Personal Health Operating System (Backend/AI Engines)

    Python

  5. RepWise-Real-Time-Gym-Coach RepWise-Real-Time-Gym-Coach Public

    RepWise End-to-end AI fitness assistant featuring pose estimation, rep counting, form correction, workout tracking, and voice coaching.

    Python

  6. Predicting-Student-Health-Risk Predicting-Student-Health-Risk Public

    Kaggle ML project for predicting student health risk using feature engineering, ensemble learning, XGBoost, LightGBM, CatBoost, and PyTorch neural networks.

    Jupyter Notebook